Main Content
"Beyond Bias: Re-imagining the terms of 'Ethical AI' in Criminal Law" by Chelsea Barabas“ (forthcoming)
5.2
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3377921
“COMPAS Risk Scales: Demonstrating Accuracy Equity and Predictive Parity” by Northpointe, Inc (Northpointe, 2016) [Read Executive Summary, Introduction, Conclusion; skim Results]
4.3
https://perma.cc/RVC7-SE8R/
“Discrimination in the Age of Algorithms” by Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Cass R. Sunstein (Preprint, 2019)
5.1
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3329669&download=yes
“Machine Bias” by Jeff Larson, Surya Mattu, Lauren Kirchner and Julia Angwin (Pro Publica, 2016).
4.2
https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing/
[OPTIONAL] “Machine Learning: A Primer: an introduction for both technical and non-technical readers” by Lizzie Turner (Medium: Artificial Intelligence, 2018)
1.4
https://medium.com/@iamlizzieturner/lets-talk-about-machine-learning-ddca914e9dd1/
“Our Fear of Artificial Intelligence” by Paul Ford (MIT Technology Review, 2015)
1.3
https://perma.cc/T4DZ-MZLN/
Pro Publica comments on “False Positives, False Negatives, and False Analyses: A Rejoinder to ‘Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And It’s Biased Against Blacks”, markup by Julia Angwin, original paper by Anthony Flores et al. (paper published in Federal Probation Journal, 2016)
4.5
https://perma.cc/KM7Q-EVW5/
“ProPublica Responds to Company’s Critique of Machine Bias Story” by Julia Angwin and Jeff Larson (Pro Publica, 2016)
4.4
https://perma.cc/579Z-BYT4/
“Stop Doing Explainable ML” by Cynthia Rudin, Talk from “Statistics at a Crossroads: Challenges and Opportunities in the Data Science Era” (2018)
3.3
https://www.youtube.com/watch?v=I0yrJz8uc5Q
"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead" by Cynthia Rudin (Nature, 2019)
3.4
https://rdcu.be/bBCPd/
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